Profile URL: https://hdl.handle.net/20.500.13091/12006
Job Title:Dr. Öğr. Üyesi
Email Address:fzgogus@ktun.edu.tr
Main Affiliation:10.02. Department of Software Engineering
Status: Current Staff
ORCID:
0000-0001-5035-7575
0000-0001-5035-7575YÖK Akademik: 9C204EFB7D6DC913
Google Scholar:
dF2mZS8AAAAJ
dF2mZS8AAAAJWeb of Science ID:
PGA-5054-2026
PGA-5054-2026Name Variants:
Solak, Fatma Z. Göğüş, F. Zehra
7 results
Scholarly Output Search Results
Now showing 1 - 7 of 7
Article Citation - Scopus: 1Automatic Sleep Stage Classification for the Obstructive Sleep Apnea(Trans Tech Publications Ltd, 2023-05-31) Özsen, Seral; Koca, Yasin; Tezel, Gülay Tezel; Solak, Fatma Zehra; Vatansev, Hulya; Kucukturk, SerkanAutomatic sleep scoring systems have been much more attention in the last decades. Whereas a wide variety of studies have been used in this subject area, the accuracies are still under acceptable limits to apply these methods to real-life data. One can find many high-accuracy studies in literature using a standard database but when it comes to using real data reaching such high performance is not straightforward. In this study, five distinct datasets were prepared using 124 persons including 93 unhealthy and 31 healthy persons. These datasets consist of time-, nonlinear-, welch-, discrete wavelet transform- and Hilbert-Huang transform features. By applying k-NN, Decision Trees, ANN, SVM, and Bagged Tree classifiers to these feature sets in various manners by using feature-selection highest classification accuracy was searched. The maximum classification accuracy was detected in the case of the Bagged Tree classifier as 95.06% with the use of 14 features among a total of 136 features. This accuracy is relatively high compared with the literature for a real-data application.Article Citation - WoS: 2Apneic Events Detection Using Different Features of Airflow Signals(MEHRAN UNIV ENGINEERING & TECHNOLOGY, 2019-01-01) Göğüş, Fatma Zehra; Tezel, GülayApneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons's Beth Isreal Hospital) databases. In order to accomplish this task, three representative feature sets namely classic feature set, amplitude feature set and descriptive model feature set were created. The performance of these feature sets were evaluated individually and in combination with the aid of the random forest classifier to detect apneic episodes. Moreover, effective features were selected by OneR Attribute Eval Feature Selection Algorithm to obtain higher performance. Selected 28 features for Apnea-ECG database and 31 features for MIT-BIH database from 54 features were applied to classifier to compare achievements. As a result, the highest classification accuracies were obtained with the usage of effective features as 96.21% for Apnea-ECG database and 92.23% for MIT-BIH database. Kappa values are also quite good (91.80 and 81.96%) and support the classification accuracies for both databases, too. The results of the study are quite promising for determining apneic events on a minute-by-minute basis.Article Comprehensive Evaluation of Preprocessing Pipeline Depth in Deep Learning-Based Brain Tumor Classification Using CNN and Vision Transformer Architectures(Springer, 2026-02-16) Solak, Fatma Z.Brain tumor classification via MRI remains a critical challenge. This study presents a systematic evaluation of how preprocessing pipeline depth influences the performance of deep learning models, including both convolutional neural networks (CNNs) and Vision Transformer (ViT) architectures. A five-stage progressive preprocessing framework (denoising, contrast enhancement, edge sharpening, gamma correction, normalization) was designed and evaluated on a balanced MRI dataset of 8,000 images (glioma, meningioma, pituitary, normal). Comprehensive analysis revealed significant accuracy improvements (up to + 4.5%) with deeper preprocessing, especially for DenseNet121 and ViT Large. Stability analysis identified ViT Base R50 and VGG19 as the most robust architectures across varying preprocessing intensities. A composite clinical balance score, integrating performance, efficiency, and parameter load, ranked ViT Base R50 as the most suitable model for clinical deployment. This study emphasizes the pivotal role of preprocessing and proposes evidence-based guidelines for its design in clinical AI.Article A Joint Fusion Framework Integrating Traditional and Deep Image Features for Improved Knee Osteoarthritis Grading(Wiley, 2026-01-01) Yilmaz, Usame; Solak, Fatma Z.Knee osteoarthritis (KOA) grading from x-ray images is important for supporting effective treatment planning. Yet it remains difficult due to the disease's complex presentation. Subtle anatomical changes add to the challenge. The subjectivity of manual evaluation further complicates the process. Such challenges highlight the importance of automated, objective, and reproducible computer-aided systems capable of leveraging complementary sources of information. In line with this, a joint fusion framework was developed to integrate optimized traditional image features with deep learning representations obtained from multiple pre-trained convolutional neural network models. Traditional features, including morphological, statistical, texture-based, and other clinically relevant descriptors, provide interpretable insights into bone structure and tissue characteristics. In parallel, deep features capture intricate spatial patterns and semantic details beyond the reach of manual modeling. For improved discrimination and efficiency, analysis of variance and linear discriminant analysis were used for selecting traditional features, while principal component analysis was applied to deep features to retain 85% variance. The two feature sets were combined using a Joint Fusion Type II approach, and class imbalance was mitigated through synthetic minority oversampling. A neural network was trained to capture interdependencies between these features. Experimental results on a benchmark KOA dataset indicated that fusion with VGG16 deep features achieved 85.39% accuracy, outperforming individual feature-based approaches. The framework maintained relatively high accuracy across all five KOA grades, including borderline cases, indicating its potential for consistent and clinically relevant KOA grading.Article Arrhythmia Detection From 12-Lead ECG with 2-Phase Feature Extraction: By Presenting the Evaluation of Atrial Fibrillation(Springer London Ltd, 2025-11-17) Erol Dogan, Gizemnur; Tezel, Gulay; Solak, Fatma Zehra; Uzbas, Betul12-Lead Electrocardiography (ECG) is an essential diagnostic tool for detecting Cardiac Arrhythmias (CAs). In this study, Arrhythmia Detection (AD) was conducted using a 12-Lead ECG dataset. The dataset underwent specific preprocessing, and a hybrid 2-Phase Feature Extraction (2-PFE) method was proposed: (1) QRS detection using the Pan-Tompkins algorithm, and (2) P-Peak detection using windowing. The study specifically focused on analyzing Atrial Fibrillation (AFIB) separately from other arrhythmia types. This approach was evaluated through three classification models: SR and NON-SR, SR and NON-SR without AFIB, SR and AFIB.Article Citation - WoS: 6Citation - Scopus: 6Identification of Apnea-Hypopnea Index Subgroups Based on Multifractal Detrended Fluctuation Analysis and Nasal Cannula Airflow Signals(INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC, 2020-04-30) Göğüş, Fatma Zehra; Tezel, Gülay; Özşen, Seral; Küççüktürk, Serkan; Vatansev, Hülya; Koca, YasinThe diagnosis of obstructive sleep apnea hypopnea syndrome (OSASH) and making decision of treatment necessity with positive airway pressure (PAP) therapy are time consuming and costly processes. There were different approaches in literature to accomplish these processes successfully and as soon as possible by using physiological signals with selected feature extraction and machine learning techniques. To reach fastest and true result, selection of optimal physiological signal(s), feature extraction and learning techniques is important. This study aimed to identify apnea hypopnea index (AHI) subgroups of 120 subjects and thus diagnose of OSASH and determine the need for PAP therapy by applying Multifractal Detrended Fluctuation Analysis (MDFA) as a feature extraction technique to only single channel nasal cannula airflow signals. After the extracted features from airflow signals with MDFA were gone through feature selection phase, the selected features were evaluated in Random Forest classifier. With the implementation of all processes, OSAHS patients were discriminated from healthy subjects with 95.83% accuracy, 96.88% sensitivity and 93.75% specificity. 93.75% sensitivities and 93.75%, 100% and 96.88% specificities were obtained for 15 <= AHI (PAP therapy necessary), 5 <= AHI<15 (require additional information for PAP therapy decision) and AHI <5 (not require PAP therapy) subgroups, respectively.Article Classification of Sleep Stages Using Psg Recording Signals(2020-10-05) Koca, Yasin; Özşen, Seral; Göğüş, Fatma Zehra; Tezel, Gülay; Küççüktürk, Serkan; Vatansev, HülyaAutomatic sleep staging is aimed within the scope of this paper. Sleep staging is a study by a sleep specialist. Since this process takes quite a long time and sleep is a method based on the knowledge and experience, it is inevitable for each person to show different results. For this, an automatic sleep staging method has been introduced. In the study, EEG (Electroencephalogram), EOG (Electrooculogram), EMG (Electromyogram) data recorded by PSG (Polysomnography) device for seven patients in Necmettin Erbakan University sleep laboratory were used. 81 different features were taken from the data in time and frequency environment. Also, PCA (Principal component analysis) and SFS (Sequential forward selection) feature selection methods were used. The classification success of the sleep phases in different machine learning methods was measured by using the received features. Linear D. (Linear Discriminant Analysis), Cubic SVM (Support vector machine), Weighted kNN (k nearest neighbor), Bagged Trees, ANN (Artificial neural network) were used as classifiers. System success was achieved with a 5 fold cross-validation method. Accuracy rates obtained were respectively 55.6%, 65.8%, 67%, 72.1%, and 69.1%.
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| Journal | Count |
|---|---|
| Avrupa Bilim ve Teknoloji Dergisi | 2 |
| Signal Image and Video Processing | 2 |
| Concurrency and Computation-Practice & Experience | 1 |
| European Journal of Science and Technology | 1 |
| Journal of Biomimetics Biomaterials and Biomedical Engineering | 1 |
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Scholarly Output
18
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12
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77/114
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3
Supervised PhD Theses
1
WoS Citation Count
9
Scopus Citation Count
7
Patents
0
Projects
0
WoS Citations per Publication
0.50
Scopus Citations per Publication
0.39
Open Access Source
10
Supervised Theses
4
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